The primary objective of the Epidemiology and Assessment Core is to conduct a comprehensive epidemiological survey of oral disease and functional status in a relatively large population-based probability sample of Mexican American and non-Hispanic White residents of three socioeconomically distinct neighborhoods: a low-income barrio, a middle- income transitional, and an upper-income suburban. Table 1 displays sample characteristics for the SAHS/SALSA cohorts by age, gender, ethnic status, and hypertensive status. Table 2 breaks the samples down by diabetic status. A total of 5,064 individuals are available for this epidemiological survey, 1,331 of whom are age 65 or older. A random sample of 1,640 stratified by age, gender, and ethnic status (Table 3) is available to describe the prevalence of oral disease and normative levels for oral function stratified by age, gender, and ethnicity. Each subproject within the OH:SALSA - RCOHA has its own specific epidemiological question(s). Data analysis strategies for the specific subprojects are described in the Research Design and Methods Section for the Subproject. Some general data analysis strategies for the epidemiological analyses include graphical and numerical summaries for each measure by sample characteristics, traditional epidemiological measures for the prevalence of oral disease and oral dysfunctions and the application of linear and nonlinear regression models to identify specific social, psychological, health and life-style factors associated with variation in oral disease and oral function. Multivariate analysis of variance is used to evaluate cross-sectional differences in oral function attributable to age, gender and ethnicity. SES status, as measured by the Duncan Index, is a potential covariate in this analysis. A preliminary test of the appropriateness and efficacy of SES is done prior to its inclusion in the analysis. Other multivariate strategies can be used to assess the relationships between measures of functional status and oral health and function. Structural equation modeling, for example, can be used to construct and test potential explanatory relationships among other masticatory measures and TMJ function.